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nietus

Anki MCP Server

by nietus
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation3/5

    The tools have overlapping purposes, as 'anki_add_note' and 'anki_add_notes' both handle adding flashcards, which could cause confusion for an agent deciding between single vs. batch operations. However, the descriptions clarify the distinction (single vs. multiple), mitigating some ambiguity. The 'anki_create_deck' tool is clearly distinct, focusing on deck management rather than note addition.

    Naming Consistency5/5

    All tool names follow a consistent 'anki_' prefix and snake_case pattern (e.g., anki_add_note, anki_add_notes, anki_create_deck), with clear verb_noun structures. This predictability makes it easy for agents to understand and navigate the tool set without confusion from mixed conventions.

    Tool Count2/5

    With only 3 tools, the server feels thin for managing Anki flashcards, a domain that typically involves operations like updating, deleting, reviewing, or searching notes and decks. The count is too low for the apparent scope, lacking essential CRUD lifecycle coverage beyond basic creation, which may limit agent functionality.

    Completeness2/5

    The tool set is severely incomplete for Anki management, covering only note addition and deck creation. Obvious gaps include updating or deleting notes/decks, listing or searching existing content, and handling reviews or scheduling—core aspects of flashcard workflows. This will likely cause agent failures when trying to perform common tasks beyond initial setup.

  • Average 2.9/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 1 of 1 community issues answered or closed in the last 6 months
    • 2 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Add') but doesn't cover critical aspects like whether this is a mutation (likely yes), error handling (e.g., if the deck doesn't exist), permissions needed, or response format. This leaves significant gaps for safe and effective tool invocation.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, direct sentence with zero wasted words, making it highly efficient and front-loaded. It immediately conveys the core purpose without unnecessary elaboration, earning full marks for conciseness.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity as a mutation operation with no annotations and no output schema, the description is insufficient. It doesn't address behavioral traits, error conditions, or return values, leaving the agent with incomplete information for reliable use in a broader context.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema has 100% description coverage, clearly documenting all three required parameters (deckName, front, back). The description adds no additional meaning beyond the schema, such as formatting examples or constraints, so it meets the baseline for adequate but not enhanced parameter guidance.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('Add') and resource ('flashcard to an Anki deck'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'anki_add_notes' (plural) or 'anki_create_deck', which would require more specificity about scope or use cases.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus alternatives like 'anki_add_notes' or 'anki_create_deck'. It lacks context about prerequisites, such as whether the deck must exist, or exclusions, leaving the agent to infer usage from tool names alone.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries full burden. It states the tool adds cards, implying a write operation, but lacks critical behavioral details: whether it requires authentication, how it handles duplicates or errors, if it's idempotent, or what the response format is. This is a significant gap for a mutation tool.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, efficient sentence with zero waste. It's front-loaded and appropriately sized for the tool's complexity, making it easy to parse quickly.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity (a write operation with 2 parameters, no annotations, and no output schema), the description is incomplete. It lacks behavioral context, error handling details, and guidance on usage versus siblings, leaving the agent with insufficient information for reliable invocation.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 50% (only 'deckName' has a description). The description adds minimal value beyond the schema, as 'Add multiple flashcards' hints at the 'cards' array but doesn't explain its structure or semantics. With partial schema coverage, the description doesn't adequately compensate for undocumented parameters.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('Add multiple flashcards') and resource ('to an Anki deck'), distinguishing it from the sibling 'anki_add_note' (singular vs. multiple). However, it doesn't specify what constitutes a 'flashcard' beyond the schema's front/back structure, leaving some ambiguity.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No explicit guidance on when to use this tool versus alternatives is provided. The description implies bulk addition, but it doesn't clarify prerequisites (e.g., deck must exist), when to use 'anki_add_note' for single cards, or how it relates to 'anki_create_deck'.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Create a new Anki deck', implying a write operation, but lacks details on permissions, error handling, or what happens if the deck already exists. This is a significant gap for a mutation tool without annotations.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, clear sentence with no wasted words. It is front-loaded and efficiently conveys the core purpose without unnecessary elaboration.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given that this is a mutation tool with no annotations and no output schema, the description is incomplete. It fails to address behavioral aspects like side effects, return values, or error conditions, which are crucial for an agent to use the tool effectively.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema description coverage is 100%, with the single parameter 'deckName' fully documented in the schema. The description does not add any additional meaning beyond what the schema provides, such as format constraints or examples, so it meets the baseline for high schema coverage.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('Create') and resource ('new Anki deck'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'anki_add_note' or 'anki_add_notes', which are about adding notes rather than creating decks, so it misses explicit sibling distinction.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus alternatives. There are no mentions of prerequisites, context, or exclusions, leaving the agent to infer usage based on the tool name alone.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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